Counterfactual Reasoning in Observational Studies
- Negar Hassanpour, Department of Computing Science, University of Alberta
To identify the appropriate action to take, an intelligent agent must infer the causal effects of every possible action choices. A prominent example is precision medicine that attempts to identify which medical procedure will benefit each individual patient the most. This requires answering counterfactual questions such as: ""Would this patient have lived longer, had she received an alternative treatment?"". In my PhD, I attempt to explore ways to address the challenges associated with causal effect estimation; with a focus on devising methods that enhance performance according to the individual-based measures (as opposed to population-based measures)
Citation
N. Hassanpour. "Counterfactual Reasoning in Observational Studies". National Conference on Artificial Intelligence (AAAI), February 2019.| Keywords: | counterfactual reasoning, machine learning |
| Category: | In Conference |
| Web Links: | Conference Link |
BibTeX
@incollection{Hassanpour:AAAI19,
author = {Negar Hassanpour},
title = {Counterfactual Reasoning in Observational Studies},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2019,
}Last Updated: February 19, 2020Submitted by Russ Greiner
